Towards efficient adjoint-based mesh optimization for predictive Large Eddy Simulation

This thesis contributes to the effective and efficient application of unsteady adjoint methods to Adaptive Mesh Refinement (AMR) for Large Eddy Simulation (LES). Three aspects, i.e., subgrid-scale model error, storage cost of high-dimensional data, and stability of the adjoint problem for turbulent flows, were studied to make adjoint-based mesh...

An enhanced algorithm for online Proper Orthogonal Decomposition and its parallelization for unsteady simulations

Proper Orthogonal Decomposition (POD) plays an important role in the analysis of complex nonlinear systems governed by partial differential equations (PDEs), since it can describe the full-order system in a simplified but representative way using a handful of dominant dynamic modes. However, determining a POD from the results of complex...

Towards adjoint-based mesh refinement for Large Eddy Simulation using reduced-order primal solutions: Preliminary 1D Burgers study

Adaptive Mesh Refinement (AMR) is potentially an effective way to automatically generate computational meshes for high-fidelity simulations such as Large Eddy Simulation (LES). When combined with adjoint methods, which are able to localize error contributions, AMR can generate meshes that are optimal for computing a physical quantity of...